Static Obstacle Detection along the Road with a Combined Method

2018 ◽  
Author(s):  
Watcharin Tangsuksant ◽  
Chikamune Wada
Keyword(s):  
Obstacle Detection ◽  
Combined Method ◽  
The Road

scholarly journals Design, manufacture and testing of a prototype obstacle-detection device at blind corners: an automated highway system technology

2020 ◽  
Author(s):  
Charles Atombo ◽  
Emmanuel Gbey ◽  
Apevienyeku Kwami Holali
Keyword(s):  
Traffic Accidents ◽  
Low Cost ◽  
Effective Area ◽  
Obstacle Detection ◽  
The Road ◽  
Vibration Sensors ◽  
Road Signs ◽  
Sensor Module ◽  
Vehicle Path ◽  
Automated Highway

Abstract Traffic accidents on highways are attributed mostly to the "invisibility" of oncoming traffic and road signs. "Speeding" also causes drivers to reduce the effective radius of the vehicle path in the curve, thus trespassing into the lane of the oncoming traffic. The main aim of this paper was to develop a multisensory obstacle-detection device that is affordable, easy to implement and easy to maintain to reduce the risk of road accidents at blind corners. An ultrasonic sensor module with a maximum measuring angle of 15° was used to ensure that a significant portion of the lane was detected at the blind corner. The sensor covered a minimum effective area of 0.5 m2 of the road for obstacle detection. Yellow light was employed to signify caution while negotiating the blind corner. Two photoresistors (PRs) were used as sensors because of the limited number of pins on the microcontroller (Arduino Uno). However, the device developed for this project achieved obstacle detection at blind corners at relatively low cost and can be accessed by all road users. In real-world applications, the use of piezoelectric accelerometers (vibration sensors) instead of PR sensors would be more desirable in order to detect not only cars but also two-wheelers.

IMPACT OF QUALITATIVE AND QUANTITATIVE METHODS ON THE EVALUATION OF STREET LIGHTING UNIFORMITY

2021 ◽  
Author(s):  
Ulrika Wänström Lindh ◽  
Annika K. Jägerbrand
Keyword(s):  
Quantitative Methods ◽  
Field Studies ◽  
Lighting Design ◽  
Combined Method ◽  
Street Lighting ◽  
Qualitative And Quantitative ◽  
The Road ◽  
Qualitative And Quantitative Methods ◽  
Road Users ◽  
Method Approach

Uniformity of lighting for pedestrians is often assumed to have been achieved in mixed traffic environments when the lighting uniformity requirements for vehicular traffic have been fulfilled. Uniformity of lighting for drivers is commonly evaluated based on quan-titative data on parameters such as overall luminance uniformity. However, methods for evaluating uniformity from the perspective of other road users are currently somewhat lacking. This study discusses qualitative and quantitative methods of assessing street lighting uniformity, and the potential implications for lighting design and the road us-ers. We used convergence design and imbedded design based on two field studies. The research purpose is twofold: first, to study if, and how, measured lighting uni-formity corresponds with visual perception. Secondly, to identify and explain the addi-tional value that a combined method approach can contribute. The study considers ex-amples of when the measured uniformity corresponds to visually perceived uniformity and when they do not correspond.

scholarly journals Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4719
Author(s):  
Malik Haris ◽  
Jin Hou
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicle ◽  
Research Area ◽  
Visual Navigation ◽  
Obstacle Detection ◽  
The Road ◽  
Markov Random ◽  
Active Research ◽  
On The Road ◽  
Active Research Area

Nowadays, autonomous vehicle is an active research area, especially after the emergence of machine vision tasks with deep learning. In such a visual navigation system for autonomous vehicle, the controller captures images and predicts information so that the autonomous vehicle can safely navigate. In this paper, we first introduced small and medium-sized obstacles that were intentionally or unintentionally left on the road, which can pose hazards for both autonomous and human driving situations. Then, we discuss Markov random field (MRF) model by fusing three potentials (gradient potential, curvature prior potential, and depth variance potential) to segment the obstacles and non-obstacles into the hazardous environment. Since the segment of obstacles is done by MRF model, we can predict the information to safely navigate the autonomous vehicle form hazardous environment on the roadway by DNN model. We found that our proposed method can segment the obstacles accuracy from the blended background road and improve the navigation skills of the autonomous vehicle.

3D Reconstruction for Road Scene with Obstacle Detection Feedback

2018 ◽  
Vol 32 (12) ◽  
pp. 1855021 ◽  
Author(s):  
Huanbing Gao ◽  
Lei Liu ◽  
Ya Tian ◽  
Shouyin Lu
Keyword(s):  
3D Reconstruction ◽  
Laser Scanning ◽  
Autonomous Driving ◽  
Calibration Method ◽  
Obstacle Detection ◽  
Reconstruction Method ◽  
Navigation Systems ◽  
Driver Assistance ◽  
The Road ◽  
Angle Measuring

This paper presented 3D reconstruction method for road scene with the help of obstacle detection. 3D reconstruction for road scene can be used in autonomous driving, driver assistance system, car navigation systems. However, some errors often rose when 3D reconstructing due to the shade from the moving object in the road scene. The presented 3D reconstruction method with obstacle detection feedback can avoid this problem. Firstly, this paper offers a framework for the 3D reconstruction of road scene by laser scanning and vision. A calibration method based on the location of horizon is proposed, and a method of attitude angle measuring based on vanishing point is proposed to revise the 3D reconstruction result. Secondly, the reconstruction framework is extended by integrating with an object recognition that can automatically detect and discriminate obstacles in the input video streams by a RANSAC approach and threshold filter, and localizes them in the 3D model. 3D reconstruction and obstacle detection are tightly integrated and benefit from each other. The experiment result verified the feasibility and practicability of the proposed method.

A New Roadside Detection Method Based on Obstacle Detection

2012 ◽  
Vol 429 ◽  
pp. 324-328
Author(s):  
Chun He Yu ◽  
Dan Ping Zhang ◽  
Rui Guo
Keyword(s):  
Mobile Robot ◽  
High Stability ◽  
Detection Method ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Laser Radar ◽  
The Road ◽  
Straight Lines

In order to provide road information for outdoor mobile robot in a complicated environment, a new roadside detection method is proposed based on obstacle detection by applying a four-layer laser radar LD_ML. Because roadside obstacles distribute alone a road, theirs fitting straight lines are parallel to the road. The roadsides detection algorithm includes four steps: first, judge if there are obstacles along roadside or not; second, extract obstacles which belong to roadsides; third, build fitting straight lines through the roadside obstacles; at last, in order to obtain steady and precise roadsides, a EKF method is performed to track the roadsides. The results of experiment have testified the road roadsides detection algorithm has high stability and reliability.

scholarly journals Simulation of Obstacle Detection of An Autonomous Car

2021 ◽  
pp. 430-435
Author(s):  
Rashmi Jain ◽  
Prachi Tamgade ◽  
R. Swaroopa ◽  
Pranoti Bhure ◽  
Srushti Shahu ◽  
...  
Keyword(s):  
Obstacle Detection ◽  
The Road ◽  
Virtual Self ◽  
Human Driver ◽  
Challenging Tasks ◽  
Video Input ◽  
Self Driving Cars

Perceiving the surroundings accurately and quickly is one of the most essential and challenging tasks for systems such as self-driving cars. view to the car making it more informed about the environment than a human driver. To build a fully virtual self-driving car, we have to build two things, Self-driving car software and virtual Self-driving car. Self-driving software can do two things one is based on video input of the road, the software can determine how to safely and effectively steer the car another is based on video input of the road, the software can determine how to safely and effectively use the car’s acceleration and braking mechanisms.

scholarly journals Conceptual Design of an Anti-collision System for Light Rail Vehicles

2021 ◽  
Vol 12 (1) ◽  
pp. 9-26
Author(s):  
Mariusz WAŻNY ◽  
Krzysztof FALKOWSKI ◽  
Mirosław WRÓBLEWSKI ◽  
Konrad WOJTOWICZ ◽  
Adam MARUT
Keyword(s):  
Image Processing ◽  
Expert System ◽  
Communication System ◽  
Obstacle Detection ◽  
Light Rail ◽  
Collision System ◽  
Multiple Sources ◽  
The Road ◽  
Rail Vehicles ◽  
Visual Light

This paper presents the concepts for an anti-collision system intended for trams. The purpose of the anti-collision system is to develop and provide information to support the driver’s decision to initiate the braking of a tram. The anti-collision system is based on the processing of data from multiple sources (obstacle detection, image processing, and visual light communication system) and an expert system. The information about the road situation is visually presented on HUD (Head-up Display) of the driver.

ROAD RECOGNITION TECHNOLOGY OF AGRICULTURAL NAVIGATION ROBOT BASED ON ROAD EDGE MOVEMENT OBSTACLE DETECTION ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 281-292 ◽  
Author(s):  
Na Yu ◽  
Qing Wang ◽  
Shichao Cao
Keyword(s):  
Motion Capture ◽  
Target Location ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Working Environment ◽  
Outdoor Environment ◽  
Analysis Model ◽  
Agricultural Robots ◽  
The Road ◽  
The Mean

In order to recognize the road effectively, agricultural robots mainly rely on the tracking and detection data of road obstacles. Traditional obstacle detection mainly studies how to use multiple fusion methods such as vision and laser to analyse structured and simplified indoor scenes. The working environment of agricultural robots is a typical unstructured outdoor environment. Therefore, based on the environmental characteristics of agricultural robot navigation, the mean displacement algorithm is introduced to detect and study the obstacles aiming at the road edge. After explaining the advantages and principle flow of the mean displacement algorithm to effectively realize motion capture, the feasibility of target location and tracking research is discussed. After that, the bottom data acquisition and analysis model is constructed based on the road navigation data of agricultural robots. To capture the movement obstacles of road edge and build the foundation of road recognition technology. In order to improve the effectiveness of motion obstacle capture and detection, a moving target detection algorithm is proposed to optimize and update the mean displacement algorithm, and constructs a feature-oriented hybrid algorithm motion capture model. The simulation results indicate that the proposed optimization model can effectively improve the tracking efficiency of non-rigid targets in outdoor environment, and the number of evaluation iterations can reach 3.5621 times per frame, which shows that the research has good theoretical and practical value.

A Study on Computational Time Reduction of Road Obstacle Detection by Parallel Image Processor

2014 ◽  
Vol 18 (5) ◽  
pp. 849-855 ◽  
Author(s):  
Yutaro Okamoto ◽  
◽  
Chinthaka Premachandra ◽  
Kiyotaka Kato
Keyword(s):  
Detection Algorithm ◽  
Obstacle Detection ◽  
Transport Systems ◽  
Computational Time ◽  
Intelligent Transport Systems ◽  
Image Processor ◽  
Intelligent Transport ◽  
The Road ◽  
Parallel Image ◽  
Time Reduction

Automatic road obstacle detection is one of the significant problem in Intelligent Transport Systems (ITS). Many studies have been conducted for this interesting problem by using on-vehicle cameras. However, those methods still needs a dozens ofmillisecondsfor image processing. To develop the quick obstacle avoidance devices for vehicles, further computational time reduction is expected. Furthermore, regarding the applications, compact hardware is also expected for implementation. Thus, we study on computational time reduction of the road obstacle detection by using a small-type parallel image processor. Here, computational time is reduced by developing an obstacle detection algorithm which is appropriated to parallel processing concept of that hardware. According to the experimental evaluation of the new proposal, we could limit computational time for eleven milliseconds with a good obstacle detection performance.

Sign in / Sign up

Export Citation Format

Share Document